Manufacturing Solutions
Industrial Data Platforms on Microsoft Azure: a decision guide for manufacturing – Part 1: From use case to Azure stack
From concept to implementation In the previous articles, we discussed architecture concepts for industrial data platforms: brownfield challenges, latency classes, batch versus streaming, the Medallion Architecture, and the question of edge versus cloud. But all these concepts remain abstract until they lead to concrete technology decisions. This article is a guide to finding the right technology foundation for
Industrial Data Platforms on Microsoft Azure: a decision guide for manufacturing – Part 2: From architecture to implementation and the most costly misunderstandings in practice
From the Azure stack to a viable architecture In the first part of this article, we translated typical manufacturing scenarios, from KPI reporting through OEE monitoring to predictive maintenance, into specific Azure stacks. We structured Azure building blocks to task clusters and demonstrated how data ingestion, storage, processing, and use interact using three example stack
Recap of the Thin[gk]athon “Manufacturing-X – Dataspace Adoption”
How can artificial intelligence (AI) models be trained across organizational boundaries without disclosing sensitive data or intellectual property?
Recap Thin[gk]athon – Taking Virtual Shopfloor to the Next Level
The Thin[gk]athon demonstrates how interdisciplinary collaboration and innovative technologies drive the digitalization of production. Interdisciplinary teams have transformed real production data into an interactive 3D world and developed new solutions for industrial challenges.
Data ingestion as the basic building block of an industrial data platform – Part 2
While the first part of the article series provided an overview of the basics of data ingestion and the various data sources, this article focuses on the specific processes of data ingestion, common challenges, and tried-and-tested solutions. Architectures and patterns for data ingestion Batch vs. microbatch vs. streaming For data ingestion in industrial data platforms,
Data ingestion as the basic building block of an industrial data platform – Part 1
Find out why data ingestion is a key component of industrial data platforms and how it optimizes efficiency and costs in the manufacturing industry. Part 1 of our series highlights methods and techniques of data ingestion as well as concepts of data storage.
Central data platform: The key to future-proof production processes
A central data platform enables the integration and networking of production data, significantly enhancing efficiency and flexibility in industrial production. By providing real-time and historical data, companies can effectively leverage innovative technologies such as Artificial Intelligence and Digital Twins. The challenges in existing production environments (Brownfield) are addressed, while the benefits of transparent data management and rapid integration of new technologies are highlighted. Future perspectives for smart manufacturing demonstrate how a holistic data strategy can optimize productivity and quality in production.
Example implementation of a digital twin exchange with the Asset Administration Shell concept
The Asset Administration Shell (AAS) collects relevant information about physical or logical units to ensure efficient production. This article describes a concrete implementation of an example in which an exchange of information between two partners involved is established with the aid of the BaSyx implementation of the AAS v3.
Cloud-native microservices in monorepos – Part 2
This series highlights the implementation of microservice architectures with monorepos and serverless cloud applications. Part 2 shows how the Nx build system can be used in this context and emphasizes the advantages of monorepos in combination with the appropriate CI/CD structure.
Cloud-native microservices in monorepos – Part 1
This series highlights the implementation of microservice architectures with monorepos and serverless cloud applications. Part 1 looks at the challenges in managing microservices in a typical multi-repo set-up and suggests integration into monorepos.